Converting analog signals into digital signals is typically done using discrete-time sampling in an analog to digital converter. This involves measuring the signal at regular, discrete time intervals. There are a number of disadvantages in using discrete-time digital signals. For example, in conventional discrete-time digital signal processors (DSP), the clock signal that triggers the sampling runs at a frequency that is at least twice the highest frequency of interest in a signal. This clock has to run continually at that frequency even if there is no signal or there is no high-frequency component that needs to be processed, and this can result in a significant waste of power.
In addition, conventional DSPs suffer from aliasing and quantization error. Aliasing occurs because the input signal mixes with the clock frequency, resulting in distortion that is present when the signal is reconstructed from the samples. Quantization error is produced by the inaccuracies inherent in turning the continuous amplitude range of an analog input signal into the discrete levels of a digitized signal, and these errors can be spread across all frequencies in a conventional DSP. Techniques such as dithering and non-uniform sampling may reduce or modify one or both of these undesired effects, but residual aliasing and/or quantization noise typically remains even after applying such techniques.